Abstract Quantum computing (QC) has emerged as a transformative technology with the potential to surpass classical computational limits, especially in complex domains like medical imaging. This study investigates the integration of QC and quantum machine learning (QML) into medical imaging, with a focus on MRI, EEG, and CT. A structured literature review was conducted to identify recent developments, focusing on studies using accessible open-source data and English-language peer-reviewed publications. By leveraging quantum principles such as superposition and entanglement, hybrid quantum-classical models have demonstrated enhanced accuracy, speed, and efficiency in diagnostic tasks. In MRI, QML has improved early detection and classification of neurological diseases like Alzheimer’s and brain tumours. In EEG analysis, quantum algorithms such as Quantum EEGNet and Quantum Support Vector Machines have shown superior performance in detecting disorders, including schizophrenia and autism spectrum disorder. Additionally, quantum algorithms for CT image reconstruction and classification have achieved faster processing with fewer artifacts and greater fidelity. Despite current hardware constraints, results highlight the promise of QC in addressing existing limitations in medical diagnostics. The findings support continued development of QML models and quantum- enhanced workflows, with potential to revolutionise clinical practice by offering more accurate, efficient, and personalised care in neurological imaging and analysis.
The therapeutic landscape for multiple sclerosis has evolved markedly in recent years, with an expanding arsenal of disease- modifying therapies offering clinicians more tools to manage the disease. This progress presents both opportunities and complexities, as treatment decisions increasingly require individualized strategies balancing efficacy, safety, and long-term outcomes. While current therapies effectively reduce inflammation and delay disease progression, they fall short in halting neurodegeneration, highlighting a critical unmet need. Treatment paradigms range from escalation strategies prioritizing safety to early high-efficacy approaches aimed at aggressive disease control. Data increasingly support early initiation of high-efficacy DMTs in patients with poor prognostic indicators, but concerns over long-term safety and tolerability remain. Shared decision-making, informed by patient preferences and evolving evidence, is central to modern MS care. The future is promising, with new therapies in advanced stages of research, which seek to exceed the limits of current therapy, to act in a targeted manner, limiting both inflammation and neurodegeneration, for better control of disease activity, with an improved safety profile.
Background: Behavioral addictions (BAs), including gambling disorder, gaming disorder, problematic pornography use, compulsive sexual behavior, and problematic internet use, have received increasing attention because of their potential impact on mental health, occupational functioning, and military readiness. Objectives: To identify military-specific risk factors, clinical and operational consequences, barriers to detection and treatment, and gaps in prevention strategies related to BAs among active-duty personnel and veterans, and to propose a framework for prevention and early intervention. Methods: A narrative evidence synthesis was conducted using studies evaluating BAs in military and veteran populations, and data were extracted regarding prevalence, risk factors, psychiatric comorbidities, functional outcomes, help-seeking barriers, and intervention approaches. Findings were analyzed thematically to identify common patterns across different BAs and to inform the development of a military-specific prevention framework. Results: Evidence consistently indicated that military personnel and veterans are at increased risk of confronting multiple vulnerability factors for BAs, including trauma exposure, deployment-related stress, social isolation, boredom, sleep disruption, risk- taking tendencies, and barriers to help-seeking. Gambling disorder and problematic gaming were the most extensively studied conditions and were associated with depression, anxiety, posttraumatic stress disorder, substance use disorders, suicidality, impaired occupational functioning, financial difficulties, and reduced operational readiness. Also, emerging evidence suggests similar associations for problematic pornography use, compulsive sexual behavior, and problematic internet use. Across studies, stigma, concerns about career repercussions, and limited awareness of available services represented important obstacles to treatment engagement. Conclusions: BAs represent an under-recognized challenge to military health and force readiness, while the available evidence supports implementing integrated prevention strategies that include routine screening, psychoeducation, commander training, confidential referral pathways, and early intervention programs. A comprehensive military-specific framework targeting behavioral addictions may improve individual well-being while reducing operational and organizational consequences.
